计算机应用研究2026，Vol.43Issue(8)：2301-2307,7.DOI:10.19734/j.issn.1001-3695.2025.12.0501
融合课程学习与自适应奖励塑形的移动机器人导航方法
Mobile robot navigation method based on integrated course learning and adaptive reward shaping
摘要
Abstract
Sparse rewards in end-to-end mobile robot navigation often lead to low learning efficiency and unstable convergence in conventional reinforcement learning methods.This study developed a deep reinforcement learning approach that integrated curriculum learning and adaptive collision entropy reward shaping.It introduced collision entropy derived from 2D LiDAR as a dynamic shaping term to quantify environmental uncertainty and guide obstacle-aware exploration.An adaptive scheduling mechanism adjusted the shaping coefficient β according to the slope of success rate variation,which balanced exploration and convergence during training.A staged curriculum progressively increased environmental difficulty to stabilize learning.Simula-tion results show that the proposed method achieves an 84.5％success rate and reduces the collision rate to 14.5％in dense environments.The method effectively alleviates sparse reward issues and improves navigation performance.关键词
深度强化学习/碰撞熵/课程学习/自适应奖励塑形/移动机器人导航Key words
deep reinforcement learning/collision entropy/curriculum learning/adaptive reward shaping/mobile robot navigation分类
信息技术与安全科学引用本文复制引用
林玉杰,吴伟林,付占悦,蔡君颖,石少雄..融合课程学习与自适应奖励塑形的移动机器人导航方法[J].计算机应用研究,2026,43(8):2301-2307,7.基金项目
广西民族大学科研基金资助项目(21KJQD20) （21KJQD20）
广西重点研发计划资助项目(桂科AB25069215) （桂科AB25069215）
广西民族大学相思湖青年学者创新团队项目(2023GXUNXSHQN06) （2023GXUNXSHQN06）
国家自然科学基金资助项目(62241302) （62241302）
